Hospital-Level Care at Home for Acutely Ill Adults: a Pilot Randomized Controlled Trial. (Levine DM, et al.)
BACKGROUND: Hospitals are standard of care for acute illness, but hospitals can be unsafe, uncomfortable, and expensive. Providing substitutive hospital-level care in a patient’s home potentially reduces cost while maintaining or improving quality, safety, and patient experience, although evidence from randomized controlled trials in the US is lacking. OBJECTIVE: Determine if home hospital care reduces cost while maintaining quality, safety, and patient experience. MAIN MEASURES: Primary outcome was direct cost of the acute care episode. Secondary outcomes included utilization, 30-day cost, physical activity, and patient experience. KEY RESULTS: Nine patients were randomized to home, 11 to usual care. Median direct cost of the acute care episode for home patients was 52% (IQR, 28%; p = 0.05) lower than for control patients. During the care episode, home patients had fewer laboratory orders (median per admission: 6 vs. 19; p < 0.01) and less often received consultations (0% vs. 27%; p = 0.04). Home patients were more physically active (median minutes, 209 vs. 78; p < 0.01), with a trend toward more sleep. No adverse events occurred in home patients, one occurred in control patients. Median direct cost for the acute care plus 30-day post-discharge period for home patients was 67% (IQR, 77%; p < 0.01) lower, with trends toward less use of home-care services (22% vs. 55%; p = 0.08) and fewer readmissions (11% vs. 36%; p=0.32). Patient experience was similar in both groups. CONCLUSIONS: The use of substitutive home hospitalization compared to in-hospital usual care reduced cost and utilization and improved physical activity.
Continuous remote monitoring with convenient wireless sensors is attractive for early detection of patient deterioration, preventing adverse events and leading to better patient care. This article presents an innovative sensor design of VitalPatch, a fully disposable wireless biosensor, for remote continuous monitoring, and details the performance assessments from bench testing and laboratory validation in 57 subjects. The bench testing results reveal that VitalPatch's QRS detection had a positive predictive value of >99% from testing with ECG databases. The accuracies of HR, BR and skin temp (in mean absolute error, MAE) from bench testing were <;5 bpm, <;1 brpm, <; 1°C respectively. The laboratory testing in 57 subjects revealed the accuracy of HR and BR to be 2.2±1.5 bpm and 1.7±0.7 brpm respectively for stationary periods. The absolute percent error in detecting steps was 4.7±4.6%, and the accuracy in detecting posture was 96.4±3.1%. Meanwhile, the specificity and sensitivity of fall detection (n=20) was found to be 100% and 93.8%, respectively. In conclusion, VitalPatch biosensor demonstrated clinically acceptable accuracies for its vital signs and actigraphy metrics applicable for continuous unobtrusive patient monitoring.
Validation of biosensor algorithms is paramount for regulated medical devices applied to patient monitoring. We present validation of breathing rate (BR) measurement using a patch medical device via a novel synthetic simulation platform, in-hospital data collection and controlled laboratory study. Single-lead ECG and triaxial body acceleration signals with variability and noise are synthetically generated and quantized for a constellation according to the input parameters of heart rate (HR) as a fundamental frequency (f c ) of ECG and reference BR as a modulating frequency (f r ). Synthetic signals are input to the BR algorithms and the performance of output BRs are evaluated for a region-of-interest of the constellation (f c /f r ≥ 3 & f c /f r ≤ 8) accounting the Nyquist and physiological varability. The performances of patch sensor's BR are also evaluated in 13 post-operative patients with reference to a clinical bedside monitor and in 57 subjects carrying out a controlled laboratory protocol with reference to capnography. The synthetic simulations revealed mean absolute error (MAE) of 0.8±0.6 brpm and standard deviation of absolute error of 0.3±0.2 brpm for the BR algorithms of patch sensor. The controlled laboratory testing revealed MAE of 1.7±0.7 brpm (n=57) for stationary conditions. The proposed simulation platform can be useful for developmental refinement or validation of BR measurement prior to testing in humans at clinical or laboratory conditions and applicable for testing other patient monitoring devices with modular modifications.